Este artigo investiga o uso de dados de alta freqüência na estimação das volatilidades diária e intradiária do IBOVESPA e no cálculo do valor em risco (VaR). Os modelos GARCH e EGARCH são usados em conjunto com métodos determinísticos de filtragem de sazonalidade para a previsão da volatilidade e do VaR intradiários. Uma comparação com o método não-paramétrico baseado no quantil empíricoé efetuada. No cálculo do VaR diário, dois métodos simples de previsão buscam captar a informação de volatilidade contida nos dados de alta freqüência. O primeiro utiliza o desvio padrão amostral com janela móvel e o segundo faz uso da técnica de alisamento exponencial. Alguns métodos tradicionais aplicados a dados diários são usados para comparação. No cálculo do VaR diário, os dois métodos baseados em dados intradiários apresentaram bom desempenho. No cálculo do VaR intradiário, os resultados mostram que a filtragem do padrão sazonalé indispensávelà obtenção de medidasúteis de volatilidades com o uso dos modelos GARCH e EGARCH. This paper investigates the use of high frequency data in the estimation of daily and intraday volatility, in order to compute value at risk (VaR) forecasts for the IBOVESPA. GARCH models and deterministic methods for the filtering of seasonal patterns are used in the computation of intraday volatility and VaR forecasts. A comparison with a non-parametric method based on the empirical quantile is done. For daily VaR two simple methods seek to extract the volatility information conveyed by the high frequency data. The first method is based on the sample standard deviation with a moving window, while the second is based on exponencially weighted moving average. Some traditional methods applied to daily data are used for benchmarking. Both methods tested
ABSTRACT. The purpose of this study is to verify the efficiency and the applicability of the Least-Squares Monte Carlo method for pricing American interest rate options. Results suggest that this technique is a promising alternative to evaluate American-style interest rate options. It provides accurate option price estimates which are very close to results provided by a binomial model. Besides, actual implementation can be easily adapted to accept different interest rate models.
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